rm(list=ls())
pacman::p_load(foreign, ggplot2, plm, reshape2, countrycode, sandwich, lmtest, MASS, 
               rworldmap, RColorBrewer, states, mice, VIM, stargazer, margins, clusterSEs, lme4, optimx,
               coefplot, gridExtra, stringr, xtable, plyr, dplyr, viridis)

##### CLEAN RAW SURVEY DATA #####

df <- read.csv("covid_wave_1.csv")
df <- df[-c(1:2),]
df <- df
df$Duration..in.seconds. <- as.numeric(as.character(df$Duration..in.seconds.))

df$Q2 <- revalue(df$Q2, c("1" = "Alabama",
                          "2" = "Alaska",
                          "3" = "Arizona",
                          "4" = "Arkansas",
                          "5" = "California",
                          "6" = "Colorado",
                          "7" = "Connecticut",
                          "8" = "Delaware",
                          "9" = "District of Columbia",
                          "10" = "Florida",
                          "11" = "Georgia",
                          "12" = "Hawaii",
                          "13" = "Idaho",
                          "14" = "Illinois",
                          "15" = "Indiana",
                          "16" = "Iowa",
                          "17" = "Kansas",
                          "18" = "Kentucky",
                          "19" = "Louisiana",
                          "20" = "Maine",
                          "21" = "Maryland",
                          "22" = "Massachusetts",
                          "23" = "Michigan",
                          "24" = "Minnesota",
                          "25" = "Mississippi",
                          "26" = "Missouri",
                          "27" = "Montana",
                          "28" = "Nebraska",
                          "29" = "Nevada",
                          "30" = "New Hampshire",
                          "31" = "New Jersey",
                          "32" = "New Mexico",
                          "33" = "New York",
                          "34" = "North Carolina",
                          "35" = "North Dakota",
                          "36" = "Ohio",
                          "37" = "Oklahoma",
                          "38" = "Oregon",
                          "39" = "Pennsylvania",
                          "40" = "Puerto Rico",
                          "41" = "Rhode Island",
                          "42" = "South Carolina",
                          "43" = "South Dakota",
                          "44" = "Tennessee",
                          "45" = "Texas",
                          "46" = "Utah",
                          "47" = "Vermont",
                          "48" = "Virginia",
                          "49" = "Washington",
                          "50" = "West Virginia",
                          "51" = "Wisconsin",
                          "52" = "Wyoming",
                          "54" = "I do not reside in any U.S. state"
))

df$Q7 <- revalue(df$Q7, c("1" = "Male",
                          "2" = "Female",
                          "3" = "Other",
                          "4" = "PNTS"))

df$Q46 <- revalue(df$Q46, c("1" = "Non-Hispanic White",
                            "4" = "Non-Hispanic Black",
                            "5" = "Hispanic",
                            "6" = "Asian",
                            "7" = "American Ind./Alaskan Native",
                            "8" = "Other"))

df$Q40 <- revalue(df$Q40, c("11" = "Less than HS",
                            "12" = "HS grad or GED",
                            "13" = "Some college",
                            "14" = "Associate degree",
                            "15" = "Bachelor degree",
                            "16" = "Master's degree",
                            "17" = "Doctorate"))

df$Q9 <- revalue(df$Q9, c("1" = "Very high",
                          "2" = "High",
                          "3" = "Low",
                          "4" = "Very low"))

df$Q10 <- revalue(df$Q10, c("1" = "Very high",
                            "2" = "High",
                            "3" = "Low",
                            "4" = "Very low"))

df$Q11 <- revalue(df$Q11, c("1" = "Very high",
                            "2" = "High",
                            "3" = "Low",
                            "4" = "Very low"))

df$Q12 <- revalue(df$Q12, c("1" = "Yes",
                            "2" = "No",
                            "3" = "PNTS"))

df$Q13 <- revalue(df$Q13, c("1" = "Very",
                            "2" = "Somewhat",
                            "3" = "Not very",
                            "4" = "Not at all"))

df$Q14 <- revalue(df$Q14, c("1" = "Yes",
                            "2" = "No"))

df$Q15 <- revalue(df$Q15, c("1" = "Very",
                            "2" = "Somewhat",
                            "3" = "Not very",
                            "4" = "Not at all"))

df$Q16 <- revalue(df$Q16, c("1" = "Very",
                            "2" = "Somewhat",
                            "3" = "Not very",
                            "4" = "Not at all"))

df$Q17 <- revalue(df$Q17, c("1" = "Yes",
                            "2" = "No"))

df$Q18 <- revalue(df$Q18, c("1" = "People stockpiling",
                            "2" = "Disruption in supply chains"))

df$Q19 <- revalue(df$Q19, c("1" = "Very",
                            "2" = "Somewhat",
                            "3" = "Not very",
                            "4" = "Not at all"))

df$Q20 <- revalue(df$Q20, c("1" = "Much better",
                            "2" = "Somewhat better",
                            "3" = "No difference",
                            "4" = "Somewhat worse",
                            "5" = "Much worse"))

df$Q21 <- revalue(df$Q21, c("1" = "Much better",
                            "2" = "Somewhat better",
                            "3" = "No difference",
                            "4" = "Somewhat worse",
                            "5" = "Much worse"))

df$Q22 <- revalue(df$Q22, c("1" = "Very important",
                            "2" = "Somewhat important",
                            "3" = "Not important"))

df$Q23_1 <- revalue(df$Q23_1, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_2 <- revalue(df$Q23_2, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_3 <- revalue(df$Q23_3, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_4 <- revalue(df$Q23_4, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_5 <- revalue(df$Q23_5, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_6 <- revalue(df$Q23_6, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_7 <- revalue(df$Q23_7, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))
df$Q23_8 <- revalue(df$Q23_8, c("1" = "Very good",
                                "2" = "Good",
                                "3" = "Neither good nor bad",
                                "4" = "Bad",
                                "5" = "Very bad"))

df$Q24 <- revalue(df$Q24, c("1" = "More",
                            "2" = "Less",
                            "3" = "No effect"))

df$Q25_11 <- df$Q25_10 <- df$Q25_8 <- df$Q25_7 <- df$Q25_6 <- df$Q25_5 <- df$Q25_3 <- df$Q25_2 <- df$Q25_1 <- NA
for(i in 1:length(df$Q25)) {
  df$Q25_1[i] <- as.numeric("1"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_2[i] <- as.numeric("2"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_3[i] <- as.numeric("3"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_5[i] <- as.numeric("5"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_6[i] <- as.numeric("6"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_7[i] <- as.numeric("7"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_8[i] <- as.numeric("8"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_10[i] <- as.numeric("10"%in%strsplit(as.character(df$Q25),",")[[i]])
  df$Q25_11[i] <- as.numeric("11"%in%strsplit(as.character(df$Q25),",")[[i]])
}
df$Q25_1[as.character(df$Q25)==""] <- NA
df$Q25_2[as.character(df$Q25)==""] <- NA
df$Q25_3[as.character(df$Q25)==""] <- NA
df$Q25_5[as.character(df$Q25)==""] <- NA
df$Q25_6[as.character(df$Q25)==""] <- NA
df$Q25_7[as.character(df$Q25)==""] <- NA
df$Q25_8[as.character(df$Q25)==""] <- NA
df$Q25_10[as.character(df$Q25)==""] <- NA
df$Q25_11[as.character(df$Q25)==""] <- NA
df$Q26_11 <- df$Q26_10 <- df$Q26_8 <- df$Q26_7 <- df$Q26_6 <- df$Q26_5 <- df$Q26_3 <- df$Q26_2 <- df$Q26_1 <- NA
for(i in 1:length(df$Q26)) {
  df$Q26_1[i] <- as.numeric("1"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_2[i] <- as.numeric("2"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_3[i] <- as.numeric("3"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_5[i] <- as.numeric("5"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_6[i] <- as.numeric("6"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_7[i] <- as.numeric("7"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_8[i] <- as.numeric("8"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_10[i] <- as.numeric("10"%in%strsplit(as.character(df$Q26),",")[[i]])
  df$Q26_11[i] <- as.numeric("11"%in%strsplit(as.character(df$Q26),",")[[i]])
}
df$Q26_1[as.character(df$Q26)==""] <- NA
df$Q26_2[as.character(df$Q26)==""] <- NA
df$Q26_3[as.character(df$Q26)==""] <- NA
df$Q26_5[as.character(df$Q26)==""] <- NA
df$Q26_6[as.character(df$Q26)==""] <- NA
df$Q26_7[as.character(df$Q26)==""] <- NA
df$Q26_8[as.character(df$Q26)==""] <- NA
df$Q26_10[as.character(df$Q26)==""] <- NA
df$Q26_11[as.character(df$Q26)==""] <- NA
df$Q27 <- revalue(df$Q27, c("1" = "Not been doing enough",
                            "2"  = "Government policies simply do not matter"))
df$Q28_11 <- df$Q28_10 <- df$Q28_8 <- df$Q28_7 <- df$Q28_6 <- df$Q28_5 <- df$Q28_3 <- df$Q28_2 <- df$Q28_1 <- NA
for(i in 1:length(df$Q28)) {
  df$Q28_1[i] <- as.numeric("1"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_2[i] <- as.numeric("2"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_3[i] <- as.numeric("3"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_5[i] <- as.numeric("5"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_6[i] <- as.numeric("6"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_7[i] <- as.numeric("7"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_8[i] <- as.numeric("8"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_10[i] <- as.numeric("10"%in%strsplit(as.character(df$Q28),",")[[i]])
  df$Q28_11[i] <- as.numeric("11"%in%strsplit(as.character(df$Q28),",")[[i]])
}
df$Q28_1[as.character(df$Q28)==""] <- NA
df$Q28_2[as.character(df$Q28)==""] <- NA
df$Q28_3[as.character(df$Q28)==""] <- NA
df$Q28_5[as.character(df$Q28)==""] <- NA
df$Q28_6[as.character(df$Q28)==""] <- NA
df$Q28_7[as.character(df$Q28)==""] <- NA
df$Q28_8[as.character(df$Q28)==""] <- NA
df$Q28_10[as.character(df$Q28)==""] <- NA
df$Q28_11[as.character(df$Q28)==""] <- NA

df$Q29 <- revalue(df$Q29, c("1" = "Cooperate with others",
                            "2"  = "Act on its own"))

df$Q43 <- revalue(df$Q43, c("1" = "Strongly support",
                            "2" = "Somewhat support",
                            "3" = "Neither support nor oppose",
                            "11" = "Somewhat oppose",
                            "12" = "Strongly oppose"))

df$Q30 <- revalue(df$Q30, c("107" = "Less than $10,000",
                            "108" = "$10,000-$19,999",
                            "109" = "$20,000-$29,999",
                            "110" = "$30,000-$39,999",
                            "111" = "$40,000-$49,999",
                            "112" = "$50,000-$59,999",
                            "113" = "$60,000-$69,999",
                            "114" = "$70,000-$79,999",
                            "115" = "$80,000-$89,999",
                            "116" = "$90,000-$99,999",
                            "117" = "$100,000-$149,999",
                            "118" = "More than $150,000",
                            "119" = "PNTS"))

df$Q31 <- revalue(df$Q31, c("1" = "Working (paid employee)",
                            "2" = "Working (self-employed)",
                            "3" = "Not working (temporary layoff)",
                            "4" = "Not working (looking for work)",
                            "5" = "Not working (retired)",
                            "6" = "Not working (disabled)",
                            "9" = "Not working (student)",
                            "10" = "Homemaker",
                            "7" = "Other",
                            "8" = "PNTS"))

df$Q32 <- revalue(df$Q32, c("1" = "Yes",
                            "2" = "No"))

df$Q33 <- revalue(df$Q33, c("1" = "Republican",
                            "2" = "Democrat",
                            "3" = "Independent",
                            "4" = "Other",
                            "5" = "No preference"))

# write.csv(df, "covid_wave_1_clean.csv")

##### MANUALLY CODE COUNTY #####

# at this point a research assistant manually associated each entry with a county
# she identified county using the county name the respondent entered and their state/ZIP
# she made sure all counties were orthographically consistent
# below we load her version of the data

df <- read.csv("covid_wave_1_clean_withcounty.csv")
df <- df[-c(441,931,938,2298),] # we omit these questionable responses

#### MERGE NYT COVID DATA #####

df$state <- df$Q2

nyt <- read.csv("us-counties.csv")
nyt1 <- subset(nyt, date == "2020-04-08")
colnames(nyt1) <- c("date","county","state","fips","nyt_cases_apr","nyt_deaths_apr")
nyt2 <- subset(nyt, date == "2020-07-10")
colnames(nyt2) <- c("date","county","state","fips","nyt_cases_jul","nyt_deaths_jul")
nyt1_benchmark <- subset(nyt, date == "2020-03-25")
colnames(nyt1_benchmark) <- c("date","county","state","fips","nyt_cases_mar","nyt_deaths_mar")
nyt1_benchmark <- subset(nyt1_benchmark, select = -date)
nyt2_benchmark <- subset(nyt, date == "2020-06-26")
colnames(nyt2_benchmark) <- c("date","county","state","fips","nyt_cases_jun","nyt_deaths_jun")
nyt2_benchmark <- subset(nyt2_benchmark, select = -date)
nyt1 <- merge(nyt1,nyt1_benchmark,by = c("county","state","fips"),all.x=TRUE,all.y=FALSE)
nyt2 <- merge(nyt2,nyt2_benchmark,by = c("county","state","fips"),all.x=TRUE,all.y=FALSE)

df$county2 <- df$county
df$county2[df$state=="New York" & df$county%in%c("Queens","Bronx","Kings","Richmond")] <- "New York City"

df <- merge(df,nyt1,by.x = c("state","county2"),by.y = c("state","county"),all.x=TRUE,all.y=FALSE)
df$nyt_cases_apr[which(is.na(df$nyt_cases_apr))] <- 0
df$nyt_deaths_apr[which(is.na(df$nyt_deaths_apr))] <- 0
df$nyt_cases_mar[which(is.na(df$nyt_cases_mar))] <- 0
df$nyt_deaths_mar[which(is.na(df$nyt_deaths_mar))] <- 0
df$nyt_cases_apr_14d <- df$nyt_cases_apr-df$nyt_cases_mar
df$nyt_deaths_apr_14d <- df$nyt_deaths_apr-df$nyt_deaths_mar

df <- merge(df,nyt2,by.x = c("state","county2"),by.y = c("state","county"),all.x=TRUE,all.y=FALSE)
df$nyt_cases_jul[which(is.na(df$nyt_cases_jul))] <- 0
df$nyt_deaths_jul[which(is.na(df$nyt_deaths_jul))] <- 0
df$nyt_cases_jun[which(is.na(df$nyt_cases_jun))] <- 0
df$nyt_deaths_jun[which(is.na(df$nyt_deaths_jun))] <- 0
df$nyt_cases_jul_14d <- df$nyt_cases_jul-df$nyt_cases_jun
df$nyt_deaths_jul_14d <- df$nyt_deaths_jul-df$nyt_deaths_jun

df$fips <- df$fips.x
df <- subset(df, select = -c(date.x,fips.x,date.y,fips.y))

df$fips[df$county=="New York City"] <- 36061
df$fips[df$county=="Queens"] <- 36081
df$fips[df$county=="Bronx"] <- 36005
df$fips[df$county=="Kings"] <- 36047
df$fips[df$county=="Richmond"] <- 36085

#### MERGE POPULATION DATA #####

census <- read.csv("co-est2019-alldata.csv")
census$fips <- paste0(census$STATE,formatC(census$COUNTY, width = 3, format = "d", flag = "0"))
census <- census[,c("fips","POPESTIMATE2019")]
colnames(census) <- c("fips","census_popestimate2019")
df <- merge(df,census,by = "fips",all.x=TRUE,all.y=FALSE)
df$census_popestimate2019[df$county2=="New York City"] <- 8336817

#### MERGE VOTE SHARE DATA #####

th <- read.csv("2016_US_County_Level_Presidential_Results.csv")
th <- th[,c(2:6,9:11)]
colnames(th) <- c("th_votes_dem","th_votes_gop","th_total_votes","th_per_dem","th_per_gop",
                  "th_state_abbr","th_county_name","fips")

df <- merge(df,th, by="fips",all.x=TRUE,all.y=FALSE)

##### CLEAN VARIABLES #####

# explanatory variables

df$covid_threat_us <- factor(as.character(df$Q9))
df$covid_threat_us <- ordered(df$covid_threat_us, levels = c("Very low","Low","High","Very high"))

df$covid_threat_self <- factor(as.character(df$Q10))
df$covid_threat_self <- ordered(df$covid_threat_self, levels = c("Very low","Low","High","Very high"))

df$covid_threat_job <- factor(as.character(df$Q11))
df$covid_threat_job <- ordered(df$covid_threat_job, levels = c("Very low","Low","High","Very high"))

df$covid_contract <- factor(as.character(df$Q12))
df$covid_contract[df$covid_contract=="PNTS"] <- NA
df$covid_contract <- factor(as.character(df$covid_contract))
levels(df$covid_contract) <- c("Not contracted","Self or loved one contracted")
df$covid_contract_dum <- as.numeric(df$covid_contract=="Self or loved one contracted")

df$covid_concern_contract <- factor(as.character(df$Q13))
df$covid_concern_contract <- ordered(df$covid_concern_contract, levels = c("Not at all","Not very","Somewhat","Very"))

df$covid_job <- factor(as.character(df$Q14))
levels(df$covid_job) <- c("Has not lost job","Self or income earner lost job to COVID-19")
df$covid_job_dum <- as.numeric(df$covid_job=="Self or income earner lost job to COVID-19")

df$covid_concern_job <- factor(as.character(df$Q15))
df$covid_concern_job <- ordered(df$covid_concern_job, levels = c("Not at all","Not very","Somewhat","Very"))

df$covid_concern_finance <- factor(as.character(df$Q16))
df$covid_concern_finance <- ordered(df$covid_concern_finance, levels = c("Not at all","Not very","Somewhat","Very"))

df$covid_stocking <- factor(as.character(df$Q17))
levels(df$covid_stocking) <- c("Has not started stocking food","Has started stocking food")

df$covid_whyshortage <- factor(as.character(df$Q18))

df$nyt_log_cases_apr <- log(df$nyt_cases_apr+1)
df$nyt_log_deaths_apr <- log(df$nyt_deaths_apr+1)
df$nyt_log_cases_apr_14d <- log(df$nyt_cases_apr_14d+1)
df$nyt_log_deaths_apr_14d <- log(df$nyt_deaths_apr_14d+1)
df$nyt_log_cases_apr_pc <- log(df$nyt_cases_apr/df$census_popestimate2019+1)
df$nyt_log_deaths_apr_pc <- log(df$nyt_deaths_apr/df$census_popestimate2019+1)
df$nyt_log_cases_apr_14d_pc <- log(df$nyt_cases_apr_14d/df$census_popestimate2019+1)
df$nyt_log_deaths_apr_14d_pc <- log(df$nyt_deaths_apr_14d/df$census_popestimate2019+1)
df$nyt_log_cases_jul <- log(df$nyt_cases_jul+1)
df$nyt_log_deaths_jul <- log(df$nyt_deaths_jul+1)
df$nyt_log_cases_jul_14d <- log(df$nyt_cases_jul_14d+1)
df$nyt_log_deaths_jul_14d <- log(df$nyt_deaths_jul_14d+1)
df$nyt_log_cases_jul_pc <- log(df$nyt_cases_jul/df$census_popestimate2019+1)
df$nyt_log_deaths_jul_pc <- log(df$nyt_deaths_jul/df$census_popestimate2019+1)
df$nyt_log_cases_jul_14d_pc <- log(df$nyt_cases_jul_14d/df$census_popestimate2019+1)
df$nyt_log_deaths_jul_14d_pc <- log(df$nyt_deaths_jul_14d/df$census_popestimate2019+1)

# covariates

df$partyid <- factor(as.character(df$Q33))
levels(df$partyid) <- c("Democrat","Independent","No preference","Other","Republican")
df$partyid2 <- df$partyid
df$partyid2[which(df$partyid%in%c("Independent","No preference","Other",""))] <- "Independent"
df$partyid2 <- factor(df$partyid2)
df$republican <- as.numeric(df$partyid=="Republican")

df$conservative <-as.numeric(as.character(df$Q34))

df$income <- factor(as.character(df$Q30))
df$income[df$income%in%c("PNTS")] <- NA
df$income <- ordered(df$income, levels = c("Less than $10,000","$10,000-$19,999", "$20,000-$29,999",
                                           "$30,000-$39,999", "$40,000-$49,999",
                                           "$50,000-$59,999", "$60,000-$69,999",
                                           "$70,000-$79,999", "$80,000-$89,999",
                                           "$90,000-$99,999", "$100,000-$149,999",
                                           "More than $150,000"))
df$income_num <- as.numeric(df$income)

df$female <- as.numeric(df$Q7=="Female")
df$age <- as.numeric(as.character(df$Q5))

df$edu <- factor(as.character(df$Q40))
df$edu <- ordered(df$edu, levels = c("Less than HS","HS grad or GED",
                                     "Some college","Associate degree","Bachelor degree",
                                     "Master's degree","Doctorate"))
df$edu_num <- as.numeric(df$edu)

df$nonwhite <- as.numeric(df$Q46!="Non-Hispanic White")

df$covid_prime <- as.numeric(df$first=="covid19")
df$covid_prime[df$first==""] <- NA

# outcomes

df$covid_help <- ordered(df$Q39_1,levels=c("10","9","8","7","6","5","4","3","2","1"))
levels(df$covid_help) <- c("1 - Respond to COVID-19 at home","2","3","4","5","6","7","8","9","10 - Help developing countries respond to COVID-19")
df$covid_help_num <- as.numeric(df$covid_help)

df$covid_contrib <- factor(as.character(df$Q43))
df$covid_contrib <- ordered(df$covid_contrib, levels = c("Strongly oppose","Somewhat oppose","Neither support nor oppose","Somewhat support","Strongly support"))
df$covid_contrib_num <- as.numeric(df$covid_contrib)
df$covid_contrib_dum <- as.numeric(df$covid_contrib_num==5)

##### SAVE MERGED DATA #####

df <- df[order(df$X),]
save(df, file="covid_wave1_merged.RData")
